@inproceedings{tong-etal-2025-pamn,
title = "{PAMN}: Multi-phase Correlation Modeling for Contrast-Enhanced 3{D} Medical Image Retrieval",
author = "Tong, Haonan and
Liu, Ke and
Zhang, Chuang and
Zhang, Xinglin and
Chen, Tao and
Hwang, Jenq-Neng and
Li, Lei",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.184/",
pages = "3456--3467",
ISBN = "979-8-89176-335-7",
abstract = "Contrast-enhanced 3D Medical imaging (e.g., CT, MRI) leverages phase sequences to uncover temporal dynamics vital for diagnosing tumors, lesions, and vascular issues. However, current retrieval models primarily focus on spatial features, neglecting phase-specific progression detailed in clinical reports. We present the **Phase-aware Memory Network (PAMN)**, a novel framework enhancing 3D medical image retrieval by fusing imaging phases with diagnostic text. PAMN creates rich radiological representations that enhance diagnostic accuracy by combining image details with clinical report context, rigorously tested on a novel phase-series dataset of 12,230 hospital CT scans. PAMN achieves an effective balance of performance and scalability in 3D radiology retrieval, outperforming state-of-the-art baselines through the robust fusion of spatial, temporal, and textual information."
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<abstract>Contrast-enhanced 3D Medical imaging (e.g., CT, MRI) leverages phase sequences to uncover temporal dynamics vital for diagnosing tumors, lesions, and vascular issues. However, current retrieval models primarily focus on spatial features, neglecting phase-specific progression detailed in clinical reports. We present the **Phase-aware Memory Network (PAMN)**, a novel framework enhancing 3D medical image retrieval by fusing imaging phases with diagnostic text. PAMN creates rich radiological representations that enhance diagnostic accuracy by combining image details with clinical report context, rigorously tested on a novel phase-series dataset of 12,230 hospital CT scans. PAMN achieves an effective balance of performance and scalability in 3D radiology retrieval, outperforming state-of-the-art baselines through the robust fusion of spatial, temporal, and textual information.</abstract>
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%0 Conference Proceedings
%T PAMN: Multi-phase Correlation Modeling for Contrast-Enhanced 3D Medical Image Retrieval
%A Tong, Haonan
%A Liu, Ke
%A Zhang, Chuang
%A Zhang, Xinglin
%A Chen, Tao
%A Hwang, Jenq-Neng
%A Li, Lei
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F tong-etal-2025-pamn
%X Contrast-enhanced 3D Medical imaging (e.g., CT, MRI) leverages phase sequences to uncover temporal dynamics vital for diagnosing tumors, lesions, and vascular issues. However, current retrieval models primarily focus on spatial features, neglecting phase-specific progression detailed in clinical reports. We present the **Phase-aware Memory Network (PAMN)**, a novel framework enhancing 3D medical image retrieval by fusing imaging phases with diagnostic text. PAMN creates rich radiological representations that enhance diagnostic accuracy by combining image details with clinical report context, rigorously tested on a novel phase-series dataset of 12,230 hospital CT scans. PAMN achieves an effective balance of performance and scalability in 3D radiology retrieval, outperforming state-of-the-art baselines through the robust fusion of spatial, temporal, and textual information.
%U https://aclanthology.org/2025.findings-emnlp.184/
%P 3456-3467
Markdown (Informal)
[PAMN: Multi-phase Correlation Modeling for Contrast-Enhanced 3D Medical Image Retrieval](https://aclanthology.org/2025.findings-emnlp.184/) (Tong et al., Findings 2025)
ACL